Bayesian Extensions to a Basic Model of Software Reliability.

Abstract

A Bayesian analysis of the software reliability model of Jelinski and Moranda is given, based upon Meinhold and Singpurwalla. Important extensions are provided to the stopping rule and prior distribution of the number of defects, as well as permitting uncertainity in the failure rate. It is easy to calculate the predictive distribution of unfound errors at the end of software testing, and to see the relative effects of uncertainty in the number of errors and in the detection efficiency. The behavior of the predictive mode and mean over time are examined as possible point estimators, but are clearly inferior to calculating the full predictive distribution. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1985
Accession Number
ADA157296

Entities

People

  • W. S. Jewell

Organizations

  • University of California, Berkeley

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Air Force
  • California
  • Classification
  • Computer Programs
  • Computer Science
  • Debugging
  • Estimators
  • Industrial Engineering
  • Infinite Series
  • Markov Processes
  • Operations Research
  • Probability
  • Random Variables
  • Reliability
  • Stochastic Processes
  • Test Methods

Fields of Study

  • Engineering

Readers

  • Computational Modeling and Simulation
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference